公开(公告)号：US11468317B2

公开(公告)日：2022-10-11

申请号：US16416921

申请日：2019-05-20

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea

IPC: G06N3/08 ,  G06N3/04 ,  H04B1/00 ,  H04B17/309

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage medium, for processing radio signals. In once aspect, a system is disclosed that includes a processor and a storage device storing computer code that includes operations. The operations may include obtaining first output data generated by a first neural network based on the first neural network processing a received radio signal, receiving, by a signal transformer, a second set of input data that includes (i) the received radio signal and (ii) the first output data, generating, by the signal transformer, data representing a transformed radio signal by applying one or more transforms to the received radio signal, providing the data representing the transformed radio signal to a second neural network, obtaining second output data generated by the second neural network, and determining based on the second output data a set of information describing the received radio signal.

公开(公告)号：US11032014B2

公开(公告)日：2021-06-08

申请号：US16744369

申请日：2020-01-16

Applicant: Virginia Tech Intellectual Properties, Inc.

Inventor： Timothy James O'Shea ,  Thomas Charles Clancy, III

IPC: H04B17/391 ,  H04B17/10 ,  H04B17/373 ,  H04L25/02 ,  G06N5/04 ,  G06N3/04 ,  H04L25/03 ,  G06N20/00 ,  H04B17/24

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.